Overview

Dataset statistics

Number of variables42
Number of observations46927
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory13.5 MiB
Average record size in memory301.0 B

Variable types

Numeric20
DateTime1
Text2
Categorical14
Boolean5

Alerts

booking_datetime is highly overall correlated with checkin_date and 2 other fieldsHigh correlation
checkin_date is highly overall correlated with booking_datetime and 1 other fieldsHigh correlation
checkout_date is highly overall correlated with booking_datetime and 1 other fieldsHigh correlation
hotel_star_rating is highly overall correlated with price_per_guest_per_nightHigh correlation
no_of_adults is highly overall correlated with no_of_room and 1 other fieldsHigh correlation
no_of_room is highly overall correlated with no_of_adultsHigh correlation
origin_country_code is highly overall correlated with original_payment_currencyHigh correlation
language is highly overall correlated with original_payment_currencyHigh correlation
original_selling_amount is highly overall correlated with amount_nights and 1 other fieldsHigh correlation
distance_booking_checkin is highly overall correlated with booking_datetimeHigh correlation
amount_guests is highly overall correlated with no_of_adultsHigh correlation
amount_nights is highly overall correlated with original_selling_amountHigh correlation
price_per_guest_per_night is highly overall correlated with hotel_star_rating and 1 other fieldsHigh correlation
original_payment_currency is highly overall correlated with origin_country_code and 1 other fieldsHigh correlation
request_nonesmoke is highly overall correlated with has_requestHigh correlation
request_highfloor is highly overall correlated with has_requestHigh correlation
request_largebed is highly overall correlated with has_requestHigh correlation
has_request is highly overall correlated with request_nonesmoke and 2 other fieldsHigh correlation
no_of_extra_bed is highly imbalanced (96.1%)Imbalance
original_payment_method is highly imbalanced (58.3%)Imbalance
original_payment_type is highly imbalanced (91.4%)Imbalance
request_latecheckin is highly imbalanced (88.8%)Imbalance
request_highfloor is highly imbalanced (57.6%)Imbalance
request_twinbeds is highly imbalanced (56.0%)Imbalance
request_airport is highly imbalanced (96.0%)Imbalance
request_earlycheckin is highly imbalanced (86.3%)Imbalance
original_selling_amount is highly skewed (γ1 = 36.55009602)Skewed
h_booking_id has unique valuesUnique
hotel_star_rating has 2060 (4.4%) zerosZeros
accommadation_type_name has 938 (2.0%) zerosZeros
no_of_children has 42690 (91.0%) zerosZeros

Reproduction

Analysis started2023-06-08 14:32:27.992204
Analysis finished2023-06-08 14:33:38.842265
Duration1 minute and 10.85 seconds
Software versionydata-profiling vv4.2.0
Download configurationconfig.json

Variables

h_booking_id
Real number (ℝ)

Distinct46927
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-1.7482761 × 1016
Minimum-9.2231941 × 1018
Maximum9.2233383 × 1018
Zeros0
Zeros (%)0.0%
Negative23545
Negative (%)50.2%
Memory size366.7 KiB
2023-06-08T17:33:38.940497image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/

Quantile statistics

Minimum-9.2231941 × 1018
5-th percentile-8.3231332 × 1018
Q1-4.6146133 × 1018
median-3.2064787 × 1016
Q34.5760991 × 1018
95-th percentile8.2846133 × 1018
Maximum9.2233383 × 1018
Range-2.1169456 × 1014
Interquartile range (IQR)9.1907124 × 1018

Descriptive statistics

Standard deviation5.3247643 × 1018
Coefficient of variation (CV)-304.57227
Kurtosis-1.1991323
Mean-1.7482761 × 1016
Median Absolute Deviation (MAD)4.5955943 × 1018
Skewness0.0033792406
Sum-8.756789 × 1018
Variance2.8353114 × 1037
MonotonicityNot monotonic
2023-06-08T17:33:39.111508image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.861445259 × 10181
 
< 0.1%
-5.859385366 × 10181
 
< 0.1%
4.102580392 × 10181
 
< 0.1%
1.795941529 × 10181
 
< 0.1%
-5.721949141 × 10181
 
< 0.1%
-4.284526584 × 10181
 
< 0.1%
2.103613272 × 10181
 
< 0.1%
5.236811047 × 10181
 
< 0.1%
-2.3832694 × 10181
 
< 0.1%
-2.788888768 × 10181
 
< 0.1%
Other values (46917) 46917
> 99.9%
ValueCountFrequency (%)
-9.223194056 × 10181
< 0.1%
-9.222713784 × 10181
< 0.1%
-9.222411208 × 10181
< 0.1%
-9.222220846 × 10181
< 0.1%
-9.220467519 × 10181
< 0.1%
-9.219746746 × 10181
< 0.1%
-9.219730436 × 10181
< 0.1%
-9.219588531 × 10181
< 0.1%
-9.219312947 × 10181
< 0.1%
-9.219139934 × 10181
< 0.1%
ValueCountFrequency (%)
9.223338324 × 10181
< 0.1%
9.223221736 × 10181
< 0.1%
9.222651807 × 10181
< 0.1%
9.222015612 × 10181
< 0.1%
9.221958225 × 10181
< 0.1%
9.221798878 × 10181
< 0.1%
9.221611986 × 10181
< 0.1%
9.221457559 × 10181
< 0.1%
9.220807687 × 10181
< 0.1%
9.220787994 × 10181
< 0.1%

booking_datetime
Real number (ℝ)

Distinct357
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean199.2134
Minimum1
Maximum365
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.7 KiB
2023-06-08T17:33:39.289372image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile95
Q1180
median206
Q3234
95-th percentile262
Maximum365
Range364
Interquartile range (IQR)54

Descriptive statistics

Standard deviation49.987481
Coefficient of variation (CV)0.2509243
Kurtosis1.8416776
Mean199.2134
Median Absolute Deviation (MAD)27
Skewness-1.0796464
Sum9348487
Variance2498.7483
MonotonicityNot monotonic
2023-06-08T17:33:39.445798image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
183 541
 
1.2%
184 526
 
1.1%
186 505
 
1.1%
205 505
 
1.1%
199 500
 
1.1%
185 497
 
1.1%
213 491
 
1.0%
187 491
 
1.0%
193 490
 
1.0%
197 488
 
1.0%
Other values (347) 41893
89.3%
ValueCountFrequency (%)
1 8
< 0.1%
2 10
< 0.1%
3 9
< 0.1%
4 3
 
< 0.1%
5 9
< 0.1%
6 5
 
< 0.1%
7 10
< 0.1%
8 16
< 0.1%
9 17
< 0.1%
10 12
< 0.1%
ValueCountFrequency (%)
365 11
< 0.1%
364 3
 
< 0.1%
363 4
 
< 0.1%
362 5
< 0.1%
361 8
< 0.1%
360 5
< 0.1%
359 1
 
< 0.1%
358 3
 
< 0.1%
357 7
< 0.1%
356 2
 
< 0.1%

checkin_date
Real number (ℝ)

Distinct105
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean226.65112
Minimum158
Maximum272
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.7 KiB
2023-06-08T17:33:39.607165image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/

Quantile statistics

Minimum158
5-th percentile186
Q1205
median226
Q3250
95-th percentile267
Maximum272
Range114
Interquartile range (IQR)45

Descriptive statistics

Standard deviation26.196808
Coefficient of variation (CV)0.11558208
Kurtosis-1.1697685
Mean226.65112
Median Absolute Deviation (MAD)23
Skewness0.0039606809
Sum10636057
Variance686.27273
MonotonicityNot monotonic
2023-06-08T17:33:39.760758image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
209 864
 
1.8%
258 801
 
1.7%
208 784
 
1.7%
230 784
 
1.7%
265 771
 
1.6%
251 748
 
1.6%
188 705
 
1.5%
223 691
 
1.5%
216 669
 
1.4%
195 666
 
1.4%
Other values (95) 39444
84.1%
ValueCountFrequency (%)
158 1
 
< 0.1%
166 1
 
< 0.1%
167 1
 
< 0.1%
168 1
 
< 0.1%
172 5
 
< 0.1%
173 3
 
< 0.1%
174 7
< 0.1%
175 13
< 0.1%
176 14
< 0.1%
177 15
< 0.1%
ValueCountFrequency (%)
272 482
1.0%
271 515
1.1%
270 406
0.9%
269 419
0.9%
268 427
0.9%
267 436
0.9%
266 547
1.2%
265 771
1.6%
264 647
1.4%
263 495
1.1%

checkout_date
Real number (ℝ)

Distinct91
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean228.64479
Minimum183
Maximum273
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.7 KiB
2023-06-08T17:33:39.921330image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/

Quantile statistics

Minimum183
5-th percentile188
Q1207
median229
Q3252
95-th percentile269
Maximum273
Range90
Interquartile range (IQR)45

Descriptive statistics

Standard deviation26.114705
Coefficient of variation (CV)0.11421518
Kurtosis-1.1822042
Mean228.64479
Median Absolute Deviation (MAD)23
Skewness0.0061513478
Sum10729614
Variance681.97779
MonotonicityNot monotonic
2023-06-08T17:33:40.080560image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
210 999
 
2.1%
231 868
 
1.8%
259 843
 
1.8%
273 792
 
1.7%
189 784
 
1.7%
196 781
 
1.7%
266 774
 
1.6%
217 773
 
1.6%
252 763
 
1.6%
238 732
 
1.6%
Other values (81) 38818
82.7%
ValueCountFrequency (%)
183 504
1.1%
184 409
0.9%
185 413
0.9%
186 403
0.9%
187 424
0.9%
188 484
1.0%
189 784
1.7%
190 423
0.9%
191 402
0.9%
192 383
0.8%
ValueCountFrequency (%)
273 792
1.7%
272 554
1.2%
271 444
0.9%
270 457
1.0%
269 454
1.0%
268 466
1.0%
267 661
1.4%
266 774
1.6%
265 552
1.2%
264 420
0.9%

hotel_id
Real number (ℝ)

Distinct24969
Distinct (%)53.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1341712.9
Minimum1
Maximum5823993
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.7 KiB
2023-06-08T17:33:40.241031image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile10917.6
Q1255829
median798535
Q32284662
95-th percentile4168202
Maximum5823993
Range5823992
Interquartile range (IQR)2028833

Descriptive statistics

Standard deviation1361519.7
Coefficient of variation (CV)1.0147624
Kurtosis0.077266214
Mean1341712.9
Median Absolute Deviation (MAD)675419
Skewness1.0483226
Sum6.296256 × 1010
Variance1.853736 × 1012
MonotonicityNot monotonic
2023-06-08T17:33:40.410540image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6452 273
 
0.6%
461790 54
 
0.1%
304294 46
 
0.1%
3080111 42
 
0.1%
337607 36
 
0.1%
185945 34
 
0.1%
1603143 34
 
0.1%
43450 33
 
0.1%
4426497 32
 
0.1%
1270365 32
 
0.1%
Other values (24959) 46311
98.7%
ValueCountFrequency (%)
1 2
< 0.1%
16 2
< 0.1%
75 2
< 0.1%
85 1
 
< 0.1%
99 3
< 0.1%
122 2
< 0.1%
140 1
 
< 0.1%
148 1
 
< 0.1%
153 1
 
< 0.1%
168 4
< 0.1%
ValueCountFrequency (%)
5823993 1
< 0.1%
5808200 1
< 0.1%
5799579 1
< 0.1%
5798032 1
< 0.1%
5797970 1
< 0.1%
5795465 1
< 0.1%
5790845 1
< 0.1%
5785518 1
< 0.1%
5771209 1
< 0.1%
5761104 1
< 0.1%

hotel_country_code
Real number (ℝ)

Distinct126
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean76.486756
Minimum0
Maximum125
Zeros380
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size366.7 KiB
2023-06-08T17:33:40.571012image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile24
Q157
median80
Q3109
95-th percentile118
Maximum125
Range125
Interquartile range (IQR)52

Descriptive statistics

Standard deviation31.100448
Coefficient of variation (CV)0.4066122
Kurtosis-0.83994882
Mean76.486756
Median Absolute Deviation (MAD)29
Skewness-0.24798175
Sum3589294
Variance967.2379
MonotonicityNot monotonic
2023-06-08T17:33:40.735570image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
57 7357
15.7%
109 6331
13.5%
80 6136
13.1%
114 4062
 
8.7%
49 2883
 
6.1%
61 2718
 
5.8%
92 2128
 
4.5%
121 2105
 
4.5%
118 1445
 
3.1%
45 1295
 
2.8%
Other values (116) 10467
22.3%
ValueCountFrequency (%)
0 380
0.8%
1 2
 
< 0.1%
2 4
 
< 0.1%
3 10
 
< 0.1%
4 114
 
0.2%
5 835
1.8%
6 1
 
< 0.1%
7 6
 
< 0.1%
8 3
 
< 0.1%
9 5
 
< 0.1%
ValueCountFrequency (%)
125 4
 
< 0.1%
124 3
 
< 0.1%
123 115
 
0.2%
122 2
 
< 0.1%
121 2105
4.5%
120 4
 
< 0.1%
119 1
 
< 0.1%
118 1445
3.1%
117 1
 
< 0.1%
116 18
 
< 0.1%
Distinct17476
Distinct (%)37.2%
Missing0
Missing (%)0.0%
Memory size366.7 KiB
Minimum1999-09-09 00:00:00
Maximum2019-04-18 10:17:00
2023-06-08T17:33:40.900128image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:33:41.057345image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

hotel_star_rating
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.2239968
Minimum-1
Maximum5
Zeros2060
Zeros (%)4.4%
Negative1
Negative (%)< 0.1%
Memory size366.7 KiB
2023-06-08T17:33:41.187999image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile1
Q13
median3
Q34
95-th percentile5
Maximum5
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1722628
Coefficient of variation (CV)0.36360543
Kurtosis0.72713638
Mean3.2239968
Median Absolute Deviation (MAD)1
Skewness-0.79401111
Sum151292.5
Variance1.3742
MonotonicityNot monotonic
2023-06-08T17:33:41.288914image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
3 14061
30.0%
4 12531
26.7%
5 5053
 
10.8%
2 5018
 
10.7%
3.5 3389
 
7.2%
0 2060
 
4.4%
2.5 1752
 
3.7%
1 1342
 
2.9%
4.5 1174
 
2.5%
1.5 546
 
1.2%
ValueCountFrequency (%)
-1 1
 
< 0.1%
0 2060
 
4.4%
1 1342
 
2.9%
1.5 546
 
1.2%
2 5018
 
10.7%
2.5 1752
 
3.7%
3 14061
30.0%
3.5 3389
 
7.2%
4 12531
26.7%
4.5 1174
 
2.5%
ValueCountFrequency (%)
5 5053
 
10.8%
4.5 1174
 
2.5%
4 12531
26.7%
3.5 3389
 
7.2%
3 14061
30.0%
2.5 1752
 
3.7%
2 5018
 
10.7%
1.5 546
 
1.2%
1 1342
 
2.9%
0 2060
 
4.4%

accommadation_type_name
Real number (ℝ)

Distinct22
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.395785
Minimum0
Maximum21
Zeros938
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size366.7 KiB
2023-06-08T17:33:41.414576image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q110
median10
Q310
95-th percentile16
Maximum21
Range21
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3.1764249
Coefficient of variation (CV)0.30554931
Kurtosis2.770737
Mean10.395785
Median Absolute Deviation (MAD)0
Skewness0.1664497
Sum487843
Variance10.089675
MonotonicityNot monotonic
2023-06-08T17:33:41.534305image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
10 34255
73.0%
16 4553
 
9.7%
5 2319
 
4.9%
9 2075
 
4.4%
19 1180
 
2.5%
0 938
 
2.0%
14 454
 
1.0%
17 278
 
0.6%
3 270
 
0.6%
18 238
 
0.5%
Other values (12) 367
 
0.8%
ValueCountFrequency (%)
0 938
2.0%
1 5
 
< 0.1%
2 93
 
0.2%
3 270
 
0.6%
4 1
 
< 0.1%
5 2319
4.9%
6 37
 
0.1%
7 88
 
0.2%
8 1
 
< 0.1%
9 2075
4.4%
ValueCountFrequency (%)
21 65
 
0.1%
20 11
 
< 0.1%
19 1180
 
2.5%
18 238
 
0.5%
17 278
 
0.6%
16 4553
9.7%
15 52
 
0.1%
14 454
 
1.0%
13 8
 
< 0.1%
12 3
 
< 0.1%
Distinct136
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size366.7 KiB
2023-06-08T17:33:41.786630image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/

Length

Max length32
Median length21
Mean length8.8196134
Min length4

Characters and Unicode

Total characters413878
Distinct characters55
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique27 ?
Unique (%)0.1%

Sample

1st rowChina
2nd rowJapan
3rd rowTaiwan
4th rowTurkey
5th rowSouth Korea
ValueCountFrequency (%)
south 6364
 
10.0%
korea 6227
 
9.8%
malaysia 6068
 
9.5%
taiwan 5059
 
8.0%
thailand 3409
 
5.4%
united 3078
 
4.8%
china 2669
 
4.2%
japan 2330
 
3.7%
hong 2241
 
3.5%
kong 2241
 
3.5%
Other values (151) 23912
37.6%
2023-06-08T17:33:42.336856image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 68559
16.6%
i 37749
 
9.1%
n 33947
 
8.2%
o 24183
 
5.8%
e 22684
 
5.5%
t 16690
 
4.0%
16667
 
4.0%
r 14661
 
3.5%
s 14628
 
3.5%
h 14514
 
3.5%
Other values (45) 149596
36.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 322079
77.8%
Uppercase Letter 75123
 
18.2%
Space Separator 16671
 
4.0%
Dash Punctuation 3
 
< 0.1%
Other Punctuation 2
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 68559
21.3%
i 37749
11.7%
n 33947
10.5%
o 24183
 
7.5%
e 22684
 
7.0%
t 16690
 
5.2%
r 14661
 
4.6%
s 14628
 
4.5%
h 14514
 
4.5%
l 14009
 
4.3%
Other values (16) 60455
18.8%
Uppercase Letter
ValueCountFrequency (%)
K 11677
15.5%
S 11036
14.7%
T 8579
11.4%
N 7190
9.6%
M 6499
8.7%
U 5347
7.1%
A 4143
 
5.5%
C 3152
 
4.2%
I 3066
 
4.1%
O 2363
 
3.1%
Other values (15) 12071
16.1%
Space Separator
ValueCountFrequency (%)
16667
> 99.9%
  4
 
< 0.1%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%
Other Punctuation
ValueCountFrequency (%)
' 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 397202
96.0%
Common 16676
 
4.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 68559
17.3%
i 37749
 
9.5%
n 33947
 
8.5%
o 24183
 
6.1%
e 22684
 
5.7%
t 16690
 
4.2%
r 14661
 
3.7%
s 14628
 
3.7%
h 14514
 
3.7%
l 14009
 
3.5%
Other values (41) 135578
34.1%
Common
ValueCountFrequency (%)
16667
99.9%
  4
 
< 0.1%
- 3
 
< 0.1%
' 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 413874
> 99.9%
None 4
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 68559
16.6%
i 37749
 
9.1%
n 33947
 
8.2%
o 24183
 
5.8%
e 22684
 
5.5%
t 16690
 
4.0%
16667
 
4.0%
r 14661
 
3.5%
s 14628
 
3.5%
h 14514
 
3.5%
Other values (44) 149592
36.1%
None
ValueCountFrequency (%)
  4
100.0%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size366.7 KiB
0
36872 
1
10055 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters46927
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 36872
78.6%
1 10055
 
21.4%

Length

2023-06-08T17:33:42.484841image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-08T17:33:42.606515image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
ValueCountFrequency (%)
0 36872
78.6%
1 10055
 
21.4%

Most occurring characters

ValueCountFrequency (%)
0 36872
78.6%
1 10055
 
21.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 46927
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 36872
78.6%
1 10055
 
21.4%

Most occurring scripts

ValueCountFrequency (%)
Common 46927
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 36872
78.6%
1 10055
 
21.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 46927
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 36872
78.6%
1 10055
 
21.4%
Distinct144
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size366.7 KiB
2023-06-08T17:33:42.785077image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/

Length

Max length32
Median length26
Mean length8.4160718
Min length4

Characters and Unicode

Total characters394941
Distinct characters57
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique34 ?
Unique (%)0.1%

Sample

1st rowChina
2nd rowJapan
3rd rowTaiwan
4th rowTurkey
5th rowSouth Korea
ValueCountFrequency (%)
south 6765
 
11.2%
korea 6634
 
11.0%
malaysia 6163
 
10.2%
taiwan 5347
 
8.9%
thailand 3514
 
5.8%
united 3211
 
5.3%
china 3075
 
5.1%
japan 2469
 
4.1%
hong 2287
 
3.8%
kong 2287
 
3.8%
Other values (162) 18660
30.9%
2023-06-08T17:33:43.154473image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 69572
17.6%
i 37621
 
9.5%
n 35710
 
9.0%
o 23356
 
5.9%
e 21766
 
5.5%
t 17532
 
4.4%
h 15545
 
3.9%
s 15086
 
3.8%
l 14393
 
3.6%
13481
 
3.4%
Other values (47) 130879
33.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 321012
81.3%
Uppercase Letter 60439
 
15.3%
Space Separator 13485
 
3.4%
Other Punctuation 3
 
< 0.1%
Open Punctuation 1
 
< 0.1%
Close Punctuation 1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 69572
21.7%
i 37621
11.7%
n 35710
11.1%
o 23356
 
7.3%
e 21766
 
6.8%
t 17532
 
5.5%
h 15545
 
4.8%
s 15086
 
4.7%
l 14393
 
4.5%
r 13380
 
4.2%
Other values (16) 57051
17.8%
Uppercase Letter
ValueCountFrequency (%)
S 11588
19.2%
K 9947
16.5%
T 8986
14.9%
M 6640
11.0%
C 3587
 
5.9%
U 3249
 
5.4%
I 3217
 
5.3%
J 2481
 
4.1%
H 2298
 
3.8%
A 2284
 
3.8%
Other values (15) 6162
10.2%
Space Separator
ValueCountFrequency (%)
13481
> 99.9%
  4
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
' 2
66.7%
& 1
33.3%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 381451
96.6%
Common 13490
 
3.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 69572
18.2%
i 37621
 
9.9%
n 35710
 
9.4%
o 23356
 
6.1%
e 21766
 
5.7%
t 17532
 
4.6%
h 15545
 
4.1%
s 15086
 
4.0%
l 14393
 
3.8%
r 13380
 
3.5%
Other values (41) 117490
30.8%
Common
ValueCountFrequency (%)
13481
99.9%
  4
 
< 0.1%
' 2
 
< 0.1%
& 1
 
< 0.1%
( 1
 
< 0.1%
) 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 394937
> 99.9%
None 4
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 69572
17.6%
i 37621
 
9.5%
n 35710
 
9.0%
o 23356
 
5.9%
e 21766
 
5.5%
t 17532
 
4.4%
h 15545
 
3.9%
s 15086
 
3.8%
l 14393
 
3.6%
13481
 
3.4%
Other values (46) 130875
33.1%
None
ValueCountFrequency (%)
  4
100.0%

no_of_adults
Real number (ℝ)

Distinct21
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.3475611
Minimum1
Maximum30
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.7 KiB
2023-06-08T17:33:43.301659image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q32
95-th percentile4
Maximum30
Range29
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.3241378
Coefficient of variation (CV)0.56404829
Kurtosis33.488263
Mean2.3475611
Median Absolute Deviation (MAD)0
Skewness4.2137414
Sum110164
Variance1.753341
MonotonicityNot monotonic
2023-06-08T17:33:43.422334image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
2 32070
68.3%
1 5481
 
11.7%
4 4492
 
9.6%
3 2905
 
6.2%
6 926
 
2.0%
5 378
 
0.8%
8 301
 
0.6%
10 101
 
0.2%
7 81
 
0.2%
12 67
 
0.1%
Other values (11) 125
 
0.3%
ValueCountFrequency (%)
1 5481
 
11.7%
2 32070
68.3%
3 2905
 
6.2%
4 4492
 
9.6%
5 378
 
0.8%
6 926
 
2.0%
7 81
 
0.2%
8 301
 
0.6%
9 49
 
0.1%
10 101
 
0.2%
ValueCountFrequency (%)
30 1
 
< 0.1%
27 2
 
< 0.1%
20 1
 
< 0.1%
18 18
 
< 0.1%
17 2
 
< 0.1%
16 16
 
< 0.1%
15 4
 
< 0.1%
14 16
 
< 0.1%
13 3
 
< 0.1%
12 67
0.1%

no_of_children
Real number (ℝ)

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.14814499
Minimum0
Maximum10
Zeros42690
Zeros (%)91.0%
Negative0
Negative (%)0.0%
Memory size366.7 KiB
2023-06-08T17:33:43.550549image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum10
Range10
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.52912642
Coefficient of variation (CV)3.5716795
Kurtosis25.197554
Mean0.14814499
Median Absolute Deviation (MAD)0
Skewness4.4201626
Sum6952
Variance0.27997477
MonotonicityNot monotonic
2023-06-08T17:33:43.677214image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0 42690
91.0%
1 2117
 
4.5%
2 1726
 
3.7%
3 251
 
0.5%
4 109
 
0.2%
5 18
 
< 0.1%
6 12
 
< 0.1%
7 2
 
< 0.1%
10 1
 
< 0.1%
8 1
 
< 0.1%
ValueCountFrequency (%)
0 42690
91.0%
1 2117
 
4.5%
2 1726
 
3.7%
3 251
 
0.5%
4 109
 
0.2%
5 18
 
< 0.1%
6 12
 
< 0.1%
7 2
 
< 0.1%
8 1
 
< 0.1%
10 1
 
< 0.1%
ValueCountFrequency (%)
10 1
 
< 0.1%
8 1
 
< 0.1%
7 2
 
< 0.1%
6 12
 
< 0.1%
5 18
 
< 0.1%
4 109
 
0.2%
3 251
 
0.5%
2 1726
 
3.7%
1 2117
 
4.5%
0 42690
91.0%

no_of_extra_bed
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size366.7 KiB
0
46416 
1
 
483
2
 
22
3
 
5
5
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters46927
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 46416
98.9%
1 483
 
1.0%
2 22
 
< 0.1%
3 5
 
< 0.1%
5 1
 
< 0.1%

Length

2023-06-08T17:33:43.790983image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-08T17:33:43.912005image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
ValueCountFrequency (%)
0 46416
98.9%
1 483
 
1.0%
2 22
 
< 0.1%
3 5
 
< 0.1%
5 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 46416
98.9%
1 483
 
1.0%
2 22
 
< 0.1%
3 5
 
< 0.1%
5 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 46927
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 46416
98.9%
1 483
 
1.0%
2 22
 
< 0.1%
3 5
 
< 0.1%
5 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 46927
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 46416
98.9%
1 483
 
1.0%
2 22
 
< 0.1%
3 5
 
< 0.1%
5 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 46927
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 46416
98.9%
1 483
 
1.0%
2 22
 
< 0.1%
3 5
 
< 0.1%
5 1
 
< 0.1%

no_of_room
Real number (ℝ)

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.140303
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.7 KiB
2023-06-08T17:33:44.014746image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile2
Maximum9
Range8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.51904538
Coefficient of variation (CV)0.45518197
Kurtosis52.885359
Mean1.140303
Median Absolute Deviation (MAD)0
Skewness5.9691302
Sum53511
Variance0.26940811
MonotonicityNot monotonic
2023-06-08T17:33:44.118941image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
1 42320
90.2%
2 3426
 
7.3%
3 751
 
1.6%
4 244
 
0.5%
5 106
 
0.2%
6 32
 
0.1%
9 20
 
< 0.1%
7 16
 
< 0.1%
8 12
 
< 0.1%
ValueCountFrequency (%)
1 42320
90.2%
2 3426
 
7.3%
3 751
 
1.6%
4 244
 
0.5%
5 106
 
0.2%
6 32
 
0.1%
7 16
 
< 0.1%
8 12
 
< 0.1%
9 20
 
< 0.1%
ValueCountFrequency (%)
9 20
 
< 0.1%
8 12
 
< 0.1%
7 16
 
< 0.1%
6 32
 
0.1%
5 106
 
0.2%
4 244
 
0.5%
3 751
 
1.6%
2 3426
 
7.3%
1 42320
90.2%

origin_country_code
Real number (ℝ)

Distinct142
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean88.098493
Minimum0
Maximum141
Zeros66
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size366.7 KiB
2023-06-08T17:33:44.267807image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile31
Q162
median94
Q3125
95-th percentile134
Maximum141
Range141
Interquartile range (IQR)63

Descriptive statistics

Standard deviation35.574492
Coefficient of variation (CV)0.40380364
Kurtosis-0.78049251
Mean88.098493
Median Absolute Deviation (MAD)31
Skewness-0.37769
Sum4134198
Variance1265.5445
MonotonicityNot monotonic
2023-06-08T17:33:44.421154image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
74 6298
13.4%
94 6185
13.2%
130 5255
11.2%
125 4308
 
9.2%
70 2530
 
5.4%
31 2494
 
5.3%
55 2424
 
5.2%
58 2263
 
4.8%
134 1960
 
4.2%
119 1926
 
4.1%
Other values (132) 11284
24.0%
ValueCountFrequency (%)
0 66
 
0.1%
1 1
 
< 0.1%
2 325
 
0.7%
3 2
 
< 0.1%
4 2
 
< 0.1%
5 3
 
< 0.1%
6 13
 
< 0.1%
7 22
 
< 0.1%
8 60
 
0.1%
9 1069
2.3%
ValueCountFrequency (%)
141 2
 
< 0.1%
140 2
 
< 0.1%
139 2
 
< 0.1%
138 131
 
0.3%
137 1362
2.9%
136 1
 
< 0.1%
135 5
 
< 0.1%
134 1960
4.2%
133 4
 
< 0.1%
132 24
 
0.1%

language
Real number (ℝ)

Distinct49
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.379462
Minimum0
Maximum48
Zeros466
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size366.7 KiB
2023-06-08T17:33:44.604029image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile8
Q18
median26
Q337
95-th percentile45
Maximum48
Range48
Interquartile range (IQR)29

Descriptive statistics

Standard deviation14.699654
Coefficient of variation (CV)0.62874219
Kurtosis-1.4965756
Mean23.379462
Median Absolute Deviation (MAD)17
Skewness0.21910009
Sum1097128
Variance216.07983
MonotonicityNot monotonic
2023-06-08T17:33:44.764687image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
8 17238
36.7%
27 6684
 
14.2%
43 5776
 
12.3%
37 3217
 
6.9%
26 2436
 
5.2%
45 2399
 
5.1%
44 1929
 
4.1%
16 1293
 
2.8%
24 765
 
1.6%
48 636
 
1.4%
Other values (39) 4554
 
9.7%
ValueCountFrequency (%)
0 466
 
1.0%
1 2
 
< 0.1%
2 13
 
< 0.1%
3 1
 
< 0.1%
4 17
 
< 0.1%
5 63
 
0.1%
6 148
 
0.3%
7 5
 
< 0.1%
8 17238
36.7%
9 480
 
1.0%
ValueCountFrequency (%)
48 636
 
1.4%
47 4
 
< 0.1%
46 65
 
0.1%
45 2399
5.1%
44 1929
 
4.1%
43 5776
12.3%
42 90
 
0.2%
41 6
 
< 0.1%
40 9
 
< 0.1%
39 229
 
0.5%

original_selling_amount
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct26481
Distinct (%)56.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean219.59182
Minimum2.1
Maximum49566.16
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.7 KiB
2023-06-08T17:33:44.923007image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/

Quantile statistics

Minimum2.1
5-th percentile17.7
Q150.585
median107.78
Q3243.01
95-th percentile750.268
Maximum49566.16
Range49564.06
Interquartile range (IQR)192.425

Descriptive statistics

Standard deviation439.9447
Coefficient of variation (CV)2.0034658
Kurtosis3515.9943
Mean219.59182
Median Absolute Deviation (MAD)72.27
Skewness36.550096
Sum10304785
Variance193551.34
MonotonicityNot monotonic
2023-06-08T17:33:45.085455image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
122.63 14
 
< 0.1%
15 12
 
< 0.1%
37.05 12
 
< 0.1%
22 11
 
< 0.1%
45 11
 
< 0.1%
19.56 11
 
< 0.1%
222.94 10
 
< 0.1%
28.46 10
 
< 0.1%
30 10
 
< 0.1%
16.86 10
 
< 0.1%
Other values (26471) 46816
99.8%
ValueCountFrequency (%)
2.1 1
< 0.1%
2.27 1
< 0.1%
2.31 1
< 0.1%
2.5 1
< 0.1%
2.59 1
< 0.1%
2.64 1
< 0.1%
2.76 1
< 0.1%
2.8 1
< 0.1%
2.82 1
< 0.1%
2.9 1
< 0.1%
ValueCountFrequency (%)
49566.16 1
< 0.1%
17942.82 1
< 0.1%
15430.5 1
< 0.1%
13015.52 1
< 0.1%
11672.15 1
< 0.1%
9562.86 1
< 0.1%
9074.52 1
< 0.1%
9065.76 1
< 0.1%
8024.56 1
< 0.1%
7437.78 1
< 0.1%
Distinct36
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size366.7 KiB
Visa
23088 
MasterCard
13875 
UNKNOWN
3210 
American Express
 
2198
JCB
 
1124
Other values (31)
3432 

Length

Max length21
Median length20
Mean length6.8831163
Min length3

Characters and Unicode

Total characters323004
Distinct characters49
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowAlipay
2nd rowAmerican Express
3rd rowVisa
4th rowMasterCard
5th rowVisa

Common Values

ValueCountFrequency (%)
Visa 23088
49.2%
MasterCard 13875
29.6%
UNKNOWN 3210
 
6.8%
American Express 2198
 
4.7%
JCB 1124
 
2.4%
Alipay 945
 
2.0%
PayPal 557
 
1.2%
MayBank2U 430
 
0.9%
UnionPay - Creditcard 292
 
0.6%
K PLUS 206
 
0.4%
Other values (26) 1002
 
2.1%

Length

2023-06-08T17:33:45.237047image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
visa 23088
45.5%
mastercard 13875
27.3%
unknown 3210
 
6.3%
american 2198
 
4.3%
express 2198
 
4.3%
jcb 1124
 
2.2%
alipay 945
 
1.9%
paypal 557
 
1.1%
maybank2u 430
 
0.8%
unionpay 339
 
0.7%
Other values (43) 2784
 
5.5%

Most occurring characters

ValueCountFrequency (%)
a 57861
17.9%
s 41874
13.0%
r 32986
10.2%
i 27345
8.5%
V 23088
 
7.1%
e 19220
 
6.0%
C 15988
 
4.9%
M 14788
 
4.6%
t 14581
 
4.5%
d 14574
 
4.5%
Other values (39) 60699
18.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 228931
70.9%
Uppercase Letter 89464
 
27.7%
Space Separator 3821
 
1.2%
Decimal Number 449
 
0.1%
Dash Punctuation 339
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 57861
25.3%
s 41874
18.3%
r 32986
14.4%
i 27345
11.9%
e 19220
 
8.4%
t 14581
 
6.4%
d 14574
 
6.4%
n 3570
 
1.6%
p 3303
 
1.4%
c 2737
 
1.2%
Other values (13) 10880
 
4.8%
Uppercase Letter
ValueCountFrequency (%)
V 23088
25.8%
C 15988
17.9%
M 14788
16.5%
N 9652
10.8%
U 4185
 
4.7%
K 3449
 
3.9%
W 3411
 
3.8%
A 3382
 
3.8%
O 3222
 
3.6%
E 2248
 
2.5%
Other values (12) 6051
 
6.8%
Decimal Number
ValueCountFrequency (%)
2 430
95.8%
7 19
 
4.2%
Space Separator
ValueCountFrequency (%)
3821
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 339
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 318395
98.6%
Common 4609
 
1.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 57861
18.2%
s 41874
13.2%
r 32986
10.4%
i 27345
8.6%
V 23088
 
7.3%
e 19220
 
6.0%
C 15988
 
5.0%
M 14788
 
4.6%
t 14581
 
4.6%
d 14574
 
4.6%
Other values (35) 56090
17.6%
Common
ValueCountFrequency (%)
3821
82.9%
2 430
 
9.3%
- 339
 
7.4%
7 19
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 323004
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 57861
17.9%
s 41874
13.0%
r 32986
10.2%
i 27345
8.5%
V 23088
 
7.1%
e 19220
 
6.0%
C 15988
 
4.9%
M 14788
 
4.6%
t 14581
 
4.5%
d 14574
 
4.5%
Other values (39) 60699
18.8%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size366.7 KiB
Credit Card
46132 
Invoice
 
612
Gift Card
 
183

Length

Max length11
Median length11
Mean length10.940035
Min length7

Characters and Unicode

Total characters513383
Distinct characters15
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCredit Card
2nd rowCredit Card
3rd rowCredit Card
4th rowCredit Card
5th rowCredit Card

Common Values

ValueCountFrequency (%)
Credit Card 46132
98.3%
Invoice 612
 
1.3%
Gift Card 183
 
0.4%

Length

2023-06-08T17:33:45.381662image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-08T17:33:45.517298image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
ValueCountFrequency (%)
card 46315
49.7%
credit 46132
49.5%
invoice 612
 
0.7%
gift 183
 
0.2%

Most occurring characters

ValueCountFrequency (%)
C 92447
18.0%
r 92447
18.0%
d 92447
18.0%
i 46927
9.1%
e 46744
9.1%
t 46315
9.0%
46315
9.0%
a 46315
9.0%
I 612
 
0.1%
n 612
 
0.1%
Other values (5) 2202
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 373826
72.8%
Uppercase Letter 93242
 
18.2%
Space Separator 46315
 
9.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 92447
24.7%
d 92447
24.7%
i 46927
12.6%
e 46744
12.5%
t 46315
12.4%
a 46315
12.4%
n 612
 
0.2%
v 612
 
0.2%
o 612
 
0.2%
c 612
 
0.2%
Uppercase Letter
ValueCountFrequency (%)
C 92447
99.1%
I 612
 
0.7%
G 183
 
0.2%
Space Separator
ValueCountFrequency (%)
46315
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 467068
91.0%
Common 46315
 
9.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
C 92447
19.8%
r 92447
19.8%
d 92447
19.8%
i 46927
10.0%
e 46744
10.0%
t 46315
9.9%
a 46315
9.9%
I 612
 
0.1%
n 612
 
0.1%
v 612
 
0.1%
Other values (4) 1590
 
0.3%
Common
ValueCountFrequency (%)
46315
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 513383
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
C 92447
18.0%
r 92447
18.0%
d 92447
18.0%
i 46927
9.1%
e 46744
9.1%
t 46315
9.0%
46315
9.0%
a 46315
9.0%
I 612
 
0.1%
n 612
 
0.1%
Other values (5) 2202
 
0.4%
Distinct50
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size366.7 KiB
MYR
6088 
KRW
5995 
TWD
5300 
USD
3685 
THB
3604 
Other values (45)
22255 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters140781
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCNY
2nd rowJPY
3rd rowTWD
4th rowTRY
5th rowKRW

Common Values

ValueCountFrequency (%)
MYR 6088
13.0%
KRW 5995
12.8%
TWD 5300
11.3%
USD 3685
 
7.9%
THB 3604
 
7.7%
CNY 2908
 
6.2%
HKD 2695
 
5.7%
JPY 2677
 
5.7%
SGD 1977
 
4.2%
IDR 1908
 
4.1%
Other values (40) 10090
21.5%

Length

2023-06-08T17:33:45.616802image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
myr 6088
13.0%
krw 5995
12.8%
twd 5300
11.3%
usd 3685
 
7.9%
thb 3604
 
7.7%
cny 2908
 
6.2%
hkd 2695
 
5.7%
jpy 2677
 
5.7%
sgd 1977
 
4.2%
idr 1908
 
4.1%
Other values (40) 10090
21.5%

Most occurring characters

ValueCountFrequency (%)
D 18718
13.3%
R 17659
12.5%
Y 11789
 
8.4%
W 11352
 
8.1%
K 9238
 
6.6%
T 9034
 
6.4%
H 8063
 
5.7%
U 7111
 
5.1%
P 6972
 
5.0%
S 6444
 
4.6%
Other values (16) 34401
24.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 140781
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
D 18718
13.3%
R 17659
12.5%
Y 11789
 
8.4%
W 11352
 
8.1%
K 9238
 
6.6%
T 9034
 
6.4%
H 8063
 
5.7%
U 7111
 
5.1%
P 6972
 
5.0%
S 6444
 
4.6%
Other values (16) 34401
24.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 140781
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
D 18718
13.3%
R 17659
12.5%
Y 11789
 
8.4%
W 11352
 
8.1%
K 9238
 
6.6%
T 9034
 
6.4%
H 8063
 
5.7%
U 7111
 
5.1%
P 6972
 
5.0%
S 6444
 
4.6%
Other values (16) 34401
24.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 140781
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
D 18718
13.3%
R 17659
12.5%
Y 11789
 
8.4%
W 11352
 
8.1%
K 9238
 
6.6%
T 9034
 
6.4%
H 8063
 
5.7%
U 7111
 
5.1%
P 6972
 
5.0%
S 6444
 
4.6%
Other values (16) 34401
24.4%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size46.0 KiB
True
30549 
False
16378 
ValueCountFrequency (%)
True 30549
65.1%
False 16378
34.9%
2023-06-08T17:33:45.733370image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size366.7 KiB
2
20542 
0
13229 
1
13156 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters46927
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row2
3rd row1
4th row0
5th row2

Common Values

ValueCountFrequency (%)
2 20542
43.8%
0 13229
28.2%
1 13156
28.0%

Length

2023-06-08T17:33:45.829653image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-08T17:33:45.950334image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
ValueCountFrequency (%)
2 20542
43.8%
0 13229
28.2%
1 13156
28.0%

Most occurring characters

ValueCountFrequency (%)
2 20542
43.8%
0 13229
28.2%
1 13156
28.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 46927
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 20542
43.8%
0 13229
28.2%
1 13156
28.0%

Most occurring scripts

ValueCountFrequency (%)
Common 46927
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 20542
43.8%
0 13229
28.2%
1 13156
28.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 46927
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 20542
43.8%
0 13229
28.2%
1 13156
28.0%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size46.0 KiB
False
36484 
True
10443 
ValueCountFrequency (%)
False 36484
77.7%
True 10443
 
22.3%
2023-06-08T17:33:46.079522image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size366.7 KiB
0.0
27599 
1.0
19328 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters140781
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 27599
58.8%
1.0 19328
41.2%

Length

2023-06-08T17:33:46.183928image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-08T17:33:46.297626image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 27599
58.8%
1.0 19328
41.2%

Most occurring characters

ValueCountFrequency (%)
0 74526
52.9%
. 46927
33.3%
1 19328
 
13.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 93854
66.7%
Other Punctuation 46927
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 74526
79.4%
1 19328
 
20.6%
Other Punctuation
ValueCountFrequency (%)
. 46927
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 140781
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 74526
52.9%
. 46927
33.3%
1 19328
 
13.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 140781
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 74526
52.9%
. 46927
33.3%
1 19328
 
13.7%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size366.7 KiB
0.0
46225 
1.0
 
702

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters140781
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 46225
98.5%
1.0 702
 
1.5%

Length

2023-06-08T17:33:46.391376image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-08T17:33:46.507578image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 46225
98.5%
1.0 702
 
1.5%

Most occurring characters

ValueCountFrequency (%)
0 93152
66.2%
. 46927
33.3%
1 702
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 93854
66.7%
Other Punctuation 46927
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 93152
99.3%
1 702
 
0.7%
Other Punctuation
ValueCountFrequency (%)
. 46927
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 140781
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 93152
66.2%
. 46927
33.3%
1 702
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 140781
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 93152
66.2%
. 46927
33.3%
1 702
 
0.5%

request_highfloor
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size366.7 KiB
0.0
42874 
1.0
 
4053

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters140781
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 42874
91.4%
1.0 4053
 
8.6%

Length

2023-06-08T17:33:46.599336image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-08T17:33:46.711076image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 42874
91.4%
1.0 4053
 
8.6%

Most occurring characters

ValueCountFrequency (%)
0 89801
63.8%
. 46927
33.3%
1 4053
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 93854
66.7%
Other Punctuation 46927
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 89801
95.7%
1 4053
 
4.3%
Other Punctuation
ValueCountFrequency (%)
. 46927
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 140781
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 89801
63.8%
. 46927
33.3%
1 4053
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 140781
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 89801
63.8%
. 46927
33.3%
1 4053
 
2.9%

request_largebed
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size366.7 KiB
0.0
36421 
1.0
10506 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters140781
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 36421
77.6%
1.0 10506
 
22.4%

Length

2023-06-08T17:33:46.804950image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-08T17:33:46.917151image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 36421
77.6%
1.0 10506
 
22.4%

Most occurring characters

ValueCountFrequency (%)
0 83348
59.2%
. 46927
33.3%
1 10506
 
7.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 93854
66.7%
Other Punctuation 46927
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 83348
88.8%
1 10506
 
11.2%
Other Punctuation
ValueCountFrequency (%)
. 46927
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 140781
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 83348
59.2%
. 46927
33.3%
1 10506
 
7.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 140781
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 83348
59.2%
. 46927
33.3%
1 10506
 
7.5%

request_twinbeds
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size366.7 KiB
0.0
42654 
1.0
4273 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters140781
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 42654
90.9%
1.0 4273
 
9.1%

Length

2023-06-08T17:33:47.018613image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-08T17:33:47.131310image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 42654
90.9%
1.0 4273
 
9.1%

Most occurring characters

ValueCountFrequency (%)
0 89581
63.6%
. 46927
33.3%
1 4273
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 93854
66.7%
Other Punctuation 46927
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 89581
95.4%
1 4273
 
4.6%
Other Punctuation
ValueCountFrequency (%)
. 46927
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 140781
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 89581
63.6%
. 46927
33.3%
1 4273
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 140781
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 89581
63.6%
. 46927
33.3%
1 4273
 
3.0%

request_airport
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size366.7 KiB
0.0
46728 
1.0
 
199

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters140781
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 46728
99.6%
1.0 199
 
0.4%

Length

2023-06-08T17:33:47.225055image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-08T17:33:47.338753image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 46728
99.6%
1.0 199
 
0.4%

Most occurring characters

ValueCountFrequency (%)
0 93655
66.5%
. 46927
33.3%
1 199
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 93854
66.7%
Other Punctuation 46927
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 93655
99.8%
1 199
 
0.2%
Other Punctuation
ValueCountFrequency (%)
. 46927
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 140781
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 93655
66.5%
. 46927
33.3%
1 199
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 140781
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 93655
66.5%
. 46927
33.3%
1 199
 
0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size366.7 KiB
0.0
46026 
1.0
 
901

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters140781
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 46026
98.1%
1.0 901
 
1.9%

Length

2023-06-08T17:33:47.430103image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-08T17:33:47.664933image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 46026
98.1%
1.0 901
 
1.9%

Most occurring characters

ValueCountFrequency (%)
0 92953
66.0%
. 46927
33.3%
1 901
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 93854
66.7%
Other Punctuation 46927
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 92953
99.0%
1 901
 
1.0%
Other Punctuation
ValueCountFrequency (%)
. 46927
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 140781
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 92953
66.0%
. 46927
33.3%
1 901
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 140781
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 92953
66.0%
. 46927
33.3%
1 901
 
0.6%

hotel_area_code
Real number (ℝ)

Distinct5057
Distinct (%)10.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3025.0395
Minimum1
Maximum5896
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.7 KiB
2023-06-08T17:33:47.780092image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile243
Q11477
median3134
Q34572
95-th percentile5602
Maximum5896
Range5895
Interquartile range (IQR)3095

Descriptive statistics

Standard deviation1733.8559
Coefficient of variation (CV)0.573168
Kurtosis-1.2487545
Mean3025.0395
Median Absolute Deviation (MAD)1549
Skewness-0.11513251
Sum1.4195603 × 108
Variance3006256.2
MonotonicityNot monotonic
2023-06-08T17:33:47.926944image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3747 718
 
1.5%
1192 571
 
1.2%
643 419
 
0.9%
4463 404
 
0.9%
606 382
 
0.8%
104 372
 
0.8%
4364 342
 
0.7%
3156 335
 
0.7%
2553 322
 
0.7%
5891 302
 
0.6%
Other values (5047) 42760
91.1%
ValueCountFrequency (%)
1 1
 
< 0.1%
2 1
 
< 0.1%
3 1
 
< 0.1%
4 7
< 0.1%
5 1
 
< 0.1%
6 5
< 0.1%
7 1
 
< 0.1%
8 1
 
< 0.1%
9 7
< 0.1%
10 1
 
< 0.1%
ValueCountFrequency (%)
5896 1
 
< 0.1%
5894 2
 
< 0.1%
5893 1
 
< 0.1%
5892 2
 
< 0.1%
5891 302
0.6%
5890 1
 
< 0.1%
5889 18
 
< 0.1%
5888 1
 
< 0.1%
5887 39
 
0.1%
5886 10
 
< 0.1%

hotel_city_code
Real number (ℝ)

Distinct2402
Distinct (%)5.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1498.0466
Minimum0
Maximum2808
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size366.7 KiB
2023-06-08T17:33:48.087631image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile140
Q1583
median1572
Q32310
95-th percentile2797
Maximum2808
Range2808
Interquartile range (IQR)1727

Descriptive statistics

Standard deviation909.02087
Coefficient of variation (CV)0.60680414
Kurtosis-1.4051834
Mean1498.0466
Median Absolute Deviation (MAD)875
Skewness-0.16917617
Sum70298832
Variance826318.94
MonotonicityNot monotonic
2023-06-08T17:33:48.232460image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2477 2525
 
5.4%
2797 1883
 
4.0%
1403 1794
 
3.8%
142 1295
 
2.8%
162 1163
 
2.5%
2249 1124
 
2.4%
437 1059
 
2.3%
2799 987
 
2.1%
1816 881
 
1.9%
2310 767
 
1.6%
Other values (2392) 33449
71.3%
ValueCountFrequency (%)
0 1
 
< 0.1%
1 2
 
< 0.1%
3 10
 
< 0.1%
4 2
 
< 0.1%
5 20
< 0.1%
6 3
 
< 0.1%
8 29
0.1%
9 1
 
< 0.1%
10 2
 
< 0.1%
11 1
 
< 0.1%
ValueCountFrequency (%)
2808 2
 
< 0.1%
2807 3
 
< 0.1%
2806 1
 
< 0.1%
2805 1
 
< 0.1%
2804 2
 
< 0.1%
2803 1
 
< 0.1%
2802 1
 
< 0.1%
2800 10
 
< 0.1%
2799 987
2.1%
2797 1883
4.0%

has_request
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size366.7 KiB
0.0
23695 
2.0
10145 
1.0
10018 
3.0
2622 
4.0
 
447

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters140781
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row0.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 23695
50.5%
2.0 10145
21.6%
1.0 10018
21.3%
3.0 2622
 
5.6%
4.0 447
 
1.0%

Length

2023-06-08T17:33:48.365723image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-08T17:33:48.495026image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 23695
50.5%
2.0 10145
21.6%
1.0 10018
21.3%
3.0 2622
 
5.6%
4.0 447
 
1.0%

Most occurring characters

ValueCountFrequency (%)
0 70622
50.2%
. 46927
33.3%
2 10145
 
7.2%
1 10018
 
7.1%
3 2622
 
1.9%
4 447
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 93854
66.7%
Other Punctuation 46927
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 70622
75.2%
2 10145
 
10.8%
1 10018
 
10.7%
3 2622
 
2.8%
4 447
 
0.5%
Other Punctuation
ValueCountFrequency (%)
. 46927
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 140781
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 70622
50.2%
. 46927
33.3%
2 10145
 
7.2%
1 10018
 
7.1%
3 2622
 
1.9%
4 447
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 140781
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 70622
50.2%
. 46927
33.3%
2 10145
 
7.2%
1 10018
 
7.1%
3 2622
 
1.9%
4 447
 
0.3%

distance_booking_checkin
Real number (ℝ)

Distinct30473
Distinct (%)64.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.655487
Minimum-1.6659722
Maximum447.20972
Zeros5
Zeros (%)< 0.1%
Negative7870
Negative (%)16.8%
Memory size366.7 KiB
2023-06-08T17:33:48.633615image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/

Quantile statistics

Minimum-1.6659722
5-th percentile-0.71944444
Q10.62465278
median9.8777778
Q337.042014
95-th percentile132.92396
Maximum447.20972
Range448.87569
Interquartile range (IQR)36.417361

Descriptive statistics

Standard deviation46.847247
Coefficient of variation (CV)1.579716
Kurtosis7.9211162
Mean29.655487
Median Absolute Deviation (MAD)10.283333
Skewness2.555511
Sum1391643
Variance2194.6646
MonotonicityNot monotonic
2023-06-08T17:33:48.799865image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.5597222222 18
 
< 0.1%
-0.5256944444 16
 
< 0.1%
-0.625 16
 
< 0.1%
-0.4513888889 16
 
< 0.1%
-0.6902777778 16
 
< 0.1%
-0.4083333333 15
 
< 0.1%
-0.4805555556 15
 
< 0.1%
-0.4722222222 15
 
< 0.1%
0.09027777778 14
 
< 0.1%
-0.6243055556 14
 
< 0.1%
Other values (30463) 46772
99.7%
ValueCountFrequency (%)
-1.665972222 1
< 0.1%
-1.575694444 1
< 0.1%
-1.572916667 1
< 0.1%
-1.544444444 1
< 0.1%
-1.533333333 1
< 0.1%
-1.523611111 1
< 0.1%
-1.502777778 1
< 0.1%
-1.498611111 1
< 0.1%
-1.491666667 1
< 0.1%
-1.484722222 1
< 0.1%
ValueCountFrequency (%)
447.2097222 1
< 0.1%
361.9833333 1
< 0.1%
361.7388889 1
< 0.1%
361.4763889 1
< 0.1%
359.23125 1
< 0.1%
357.0472222 1
< 0.1%
356.2979167 1
< 0.1%
355.4930556 1
< 0.1%
353.1513889 1
< 0.1%
350.5215278 1
< 0.1%

amount_guests
Real number (ℝ)

Distinct23
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.4957061
Minimum1
Maximum30
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.7 KiB
2023-06-08T17:33:48.935504image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q33
95-th percentile5
Maximum30
Range29
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.4588569
Coefficient of variation (CV)0.58454674
Kurtosis26.962429
Mean2.4957061
Median Absolute Deviation (MAD)0
Skewness3.7545332
Sum117116
Variance2.1282633
MonotonicityNot monotonic
2023-06-08T17:33:49.059578image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
2 29487
62.8%
4 5766
 
12.3%
1 5140
 
11.0%
3 3851
 
8.2%
6 1116
 
2.4%
5 707
 
1.5%
8 353
 
0.8%
7 125
 
0.3%
10 120
 
0.3%
9 77
 
0.2%
Other values (13) 185
 
0.4%
ValueCountFrequency (%)
1 5140
 
11.0%
2 29487
62.8%
3 3851
 
8.2%
4 5766
 
12.3%
5 707
 
1.5%
6 1116
 
2.4%
7 125
 
0.3%
8 353
 
0.8%
9 77
 
0.2%
10 120
 
0.3%
ValueCountFrequency (%)
30 1
 
< 0.1%
27 2
 
< 0.1%
24 1
 
< 0.1%
21 1
 
< 0.1%
20 6
 
< 0.1%
18 17
< 0.1%
17 3
 
< 0.1%
16 16
< 0.1%
15 6
 
< 0.1%
14 22
< 0.1%

amount_nights
Real number (ℝ)

Distinct29
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.993671
Minimum1
Maximum30
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.7 KiB
2023-06-08T17:33:49.195537image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile5
Maximum30
Range29
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.7459906
Coefficient of variation (CV)0.87576665
Kurtosis44.530156
Mean1.993671
Median Absolute Deviation (MAD)0
Skewness4.7506128
Sum93557
Variance3.0484831
MonotonicityNot monotonic
2023-06-08T17:33:49.322200image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
1 25336
54.0%
2 10657
22.7%
3 5353
 
11.4%
4 2625
 
5.6%
5 1275
 
2.7%
6 550
 
1.2%
7 513
 
1.1%
8 158
 
0.3%
10 116
 
0.2%
9 101
 
0.2%
Other values (19) 243
 
0.5%
ValueCountFrequency (%)
1 25336
54.0%
2 10657
22.7%
3 5353
 
11.4%
4 2625
 
5.6%
5 1275
 
2.7%
6 550
 
1.2%
7 513
 
1.1%
8 158
 
0.3%
9 101
 
0.2%
10 116
 
0.2%
ValueCountFrequency (%)
30 11
< 0.1%
29 1
 
< 0.1%
28 7
< 0.1%
27 2
 
< 0.1%
26 2
 
< 0.1%
25 1
 
< 0.1%
23 3
 
< 0.1%
22 1
 
< 0.1%
21 10
< 0.1%
20 12
< 0.1%

price_per_guest_per_night
Real number (ℝ)

Distinct26848
Distinct (%)57.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44.164547
Minimum0.083333333
Maximum1770.22
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.7 KiB
2023-06-08T17:33:49.475787image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/

Quantile statistics

Minimum0.083333333
5-th percentile7.805
Q117.13875
median31.22
Q354.9525
95-th percentile122.70175
Maximum1770.22
Range1770.1367
Interquartile range (IQR)37.81375

Descriptive statistics

Standard deviation45.586819
Coefficient of variation (CV)1.0322039
Kurtosis80.290278
Mean44.164547
Median Absolute Deviation (MAD)16.725
Skewness5.1722562
Sum2072509.7
Variance2078.1581
MonotonicityNot monotonic
2023-06-08T17:33:49.630990image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.5 16
 
< 0.1%
9.78 15
 
< 0.1%
6 14
 
< 0.1%
26.17 14
 
< 0.1%
7 13
 
< 0.1%
15 13
 
< 0.1%
122.63 13
 
< 0.1%
13.5 13
 
< 0.1%
7.5 13
 
< 0.1%
14.23 12
 
< 0.1%
Other values (26838) 46791
99.7%
ValueCountFrequency (%)
0.08333333333 1
< 0.1%
0.1923333333 1
< 0.1%
0.4233333333 1
< 0.1%
0.4875 1
< 0.1%
0.545 1
< 0.1%
0.570625 1
< 0.1%
0.6796875 1
< 0.1%
0.8333333333 1
< 0.1%
0.9225 1
< 0.1%
1 1
< 0.1%
ValueCountFrequency (%)
1770.22 1
< 0.1%
1167.215 1
< 0.1%
915.3983333 1
< 0.1%
813.24 1
< 0.1%
739.645 1
< 0.1%
733.425 1
< 0.1%
730.28 1
< 0.1%
717.035 1
< 0.1%
715.705 1
< 0.1%
686.415 1
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size46.0 KiB
False
41347 
True
5580 
ValueCountFrequency (%)
False 41347
88.1%
True 5580
 
11.9%
2023-06-08T17:33:49.775477image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/

pay_now
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size46.0 KiB
True
35335 
False
11592 
ValueCountFrequency (%)
True 35335
75.3%
False 11592
 
24.7%
2023-06-08T17:33:49.875210image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/

is_cancel
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size46.0 KiB
False
34250 
True
12677 
ValueCountFrequency (%)
False 34250
73.0%
True 12677
 
27.0%
2023-06-08T17:33:49.976976image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/

Interactions

2023-06-08T17:33:34.299944image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:32:39.102137image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:32:42.263742image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:32:45.341496image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:32:48.222172image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:32:51.271834image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:32:54.372076image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:32:57.244723image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:33:00.229245image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:33:02.954993image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:33:05.896956image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:33:08.604697image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:33:11.569863image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:33:14.433541image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:33:17.142668image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:33:20.069326image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:33:22.922763image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:33:25.810497image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:33:28.549943image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:33:31.374457image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:33:34.461475image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:32:39.278702image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:32:42.438276image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:32:45.498170image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:32:48.376759image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:32:51.431407image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:32:54.527620image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:32:57.406289image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:33:00.383782image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
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2023-06-08T17:33:06.047553image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
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2023-06-08T17:33:07.396029image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:33:10.164401image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:33:13.145146image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:33:15.932140image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:33:18.815795image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:33:21.641067image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:33:24.574197image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:33:27.317472image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:33:30.089421image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:33:33.052181image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:33:36.074757image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:32:41.030616image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:32:44.064261image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:32:47.087146image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:32:50.099652image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:32:53.188959image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:32:56.128959image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:32:59.082021image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:33:01.864166image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:33:04.661530image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:33:07.533422image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:33:10.313950image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:33:13.290443image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:33:16.073795image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:33:18.962401image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:33:21.789664image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:33:24.714782image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:33:27.466112image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:33:30.235045image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:33:33.200747image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:33:36.210428image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:32:41.176449image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:32:44.207877image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:32:47.226064image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:32:50.232337image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:32:53.325006image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:32:56.265595image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:32:59.215703image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:33:01.992870image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:33:04.790238image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:33:07.660131image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:33:10.452408image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:33:13.429224image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:33:16.199460image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:33:19.096170image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:33:21.924305image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:33:24.847427image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:33:27.598761image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:33:30.373620image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:33:33.330437image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:33:36.354011image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:32:41.323270image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:32:44.351636image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:32:47.373714image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:32:50.369929image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:32:53.468621image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:32:56.411204image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:32:59.364316image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:33:02.123470image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:33:05.057473image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:33:07.791325image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:33:10.591039image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:33:13.567892image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:33:16.331107image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:33:19.231248image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:33:22.063891image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:33:24.984100image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:33:27.729408image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:33:30.514244image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:33:33.471023image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:33:36.500657image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:32:41.474864image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:32:44.496073image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:32:47.509353image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:32:50.509861image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:32:53.625707image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:32:56.555299image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:32:59.518943image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:33:02.262988image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:33:05.201127image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:33:07.928963image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:33:10.729672image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:33:13.709514image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:33:16.473684image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:33:19.371870image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:33:22.206549image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:33:25.120735image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:33:27.868996image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:33:30.658857image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:33:33.609705image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:33:36.641241image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:32:41.641419image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:32:44.649663image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:32:47.645929image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:32:50.641471image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:32:53.758391image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:32:56.688042image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:32:59.652190image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:33:02.410993image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:33:05.338721image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:33:08.060555image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:33:10.865303image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:33:13.847106image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:33:16.599394image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:33:19.510545image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:33:22.347258image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:33:25.251387image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:33:28.000682image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:33:30.792487image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:33:33.746300image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:33:36.794830image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:32:41.789068image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:32:44.899302image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:32:47.775583image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:32:50.769128image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:32:53.896959image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:32:56.820857image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:32:59.783707image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:33:02.539596image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:33:05.475355image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:33:08.189211image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:33:11.002935image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:33:13.987731image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:33:16.731036image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:33:19.639155image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:33:22.487430image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:33:25.380998image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:33:28.128756image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:33:30.955053image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:33:33.876641image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:33:36.955403image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:32:41.950593image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:32:45.041541image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:32:47.926962image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:32:50.907796image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:32:54.036714image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:32:56.961519image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:32:59.927321image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:33:02.675235image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:33:05.613716image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:33:08.324449image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:33:11.146553image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:33:14.128355image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:33:16.862608image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:33:19.780831image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:33:22.630552image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:33:25.523265image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:33:28.263394image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:33:31.089694image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:33:34.018876image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:33:37.114974image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:32:42.098717image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:32:45.187111image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:32:48.066591image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:32:51.055413image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:32:54.182587image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:32:57.098115image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:33:00.078918image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:33:02.804395image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:33:05.749391image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:33:08.462129image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:33:11.286622image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:33:14.277993image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:33:17.003297image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:33:19.921757image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:33:22.768182image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:33:25.659938image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:33:28.401028image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:33:31.227324image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-08T17:33:34.151303image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/

Correlations

2023-06-08T17:33:50.134172image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
h_booking_idbooking_datetimecheckin_datecheckout_datehotel_idhotel_country_codehotel_star_ratingaccommadation_type_nameno_of_adultsno_of_childrenno_of_roomorigin_country_codelanguageoriginal_selling_amounthotel_area_codehotel_city_codedistance_booking_checkinamount_guestsamount_nightsprice_per_guest_per_nightguest_is_not_the_customerno_of_extra_bedoriginal_payment_methodoriginal_payment_typeoriginal_payment_currencyis_user_logged_incancellation_policy_codeis_first_bookingrequest_nonesmokerequest_latecheckinrequest_highfloorrequest_largebedrequest_twinbedsrequest_airportrequest_earlycheckinhas_requestcostumer_guest_same_nationpay_nowis_cancel
h_booking_id1.0000.000-0.007-0.007-0.003-0.0010.0040.0050.0030.0010.007-0.0010.0090.006-0.0000.002-0.0030.0030.0030.0050.0080.0030.0040.0000.0090.0000.0040.0000.0000.0000.0070.0000.0000.0000.0000.0000.0000.0000.004
booking_datetime0.0001.0000.6480.6370.0490.067-0.067-0.053-0.088-0.105-0.0700.051-0.092-0.3180.021-0.017-0.614-0.121-0.242-0.2020.0480.0200.0480.0280.0850.1130.0760.0260.2510.0410.0430.1780.0650.0500.0340.1420.0660.2820.265
checkin_date-0.0070.6481.0000.9980.037-0.009-0.014-0.013-0.034-0.044-0.029-0.012-0.031-0.0760.0080.003-0.010-0.048-0.070-0.0290.0140.0080.0200.0130.0560.0110.0170.0120.1820.0200.0240.1220.0510.0140.0210.0980.0160.0260.021
checkout_date-0.0070.6370.9981.0000.036-0.014-0.013-0.013-0.035-0.043-0.028-0.018-0.035-0.0440.0070.0040.006-0.049-0.019-0.0230.0160.0110.0230.0160.0550.0150.0160.0060.1810.0200.0210.1200.0510.0120.0190.0970.0090.0230.022
hotel_id-0.0030.0490.0370.0361.0000.071-0.171-0.154-0.022-0.048-0.0310.0640.032-0.1100.0060.026-0.034-0.038-0.021-0.1180.0410.0280.0660.0620.0670.0580.1600.0220.0210.0140.0260.0290.0220.0040.0140.0230.0350.1630.117
hotel_country_code-0.0010.067-0.009-0.0140.0711.000-0.0770.0270.1050.0030.0040.4750.132-0.1760.0130.067-0.1060.095-0.074-0.2430.1070.0230.1440.1030.4630.0750.2080.0960.0410.0590.0400.0970.0210.0180.0420.0440.1440.2120.200
hotel_star_rating0.004-0.067-0.014-0.013-0.171-0.0771.0000.3280.0940.0780.036-0.0990.0080.445-0.002-0.0240.1120.1150.0600.5090.0480.0210.1000.0550.1080.0290.1010.0380.0230.0110.1320.0890.0720.0280.0450.0720.0370.0960.051
accommadation_type_name0.005-0.053-0.013-0.013-0.1540.0270.3281.0000.1520.0630.030-0.007-0.0130.222-0.017-0.0010.0690.1580.0160.2150.0450.0280.0550.0220.0860.0220.0870.0400.0430.0160.0710.0490.0460.0260.0230.0410.0330.0460.062
no_of_adults0.003-0.088-0.034-0.035-0.0220.1050.0940.1521.0000.0470.5290.0890.0160.2580.0120.0130.1190.9030.010-0.1000.0320.1060.0000.0190.0360.0180.0270.0080.0220.0000.0090.0690.0580.0220.0000.0140.0080.0590.045
no_of_children0.001-0.105-0.044-0.043-0.0480.0030.0780.0630.0471.0000.064-0.0090.0580.161-0.0180.0170.1250.4320.049-0.0150.0200.0370.0250.0070.0440.0060.0160.0380.0170.0060.0080.0460.0190.0110.0200.0110.0140.0910.043
no_of_room0.007-0.070-0.029-0.028-0.0310.0040.0360.0300.5290.0641.0000.0160.0150.219-0.005-0.0110.0850.4990.0340.0080.0440.1020.0080.0100.0350.0200.0060.0080.0110.0000.0070.0310.0530.0220.0000.0050.0210.0470.027
origin_country_code-0.0010.051-0.012-0.0180.0640.475-0.099-0.0070.089-0.0090.0161.0000.095-0.1310.0200.021-0.1020.075-0.092-0.1540.1310.0200.2250.3100.8060.0790.1010.1190.0790.0510.0910.1090.0580.0320.0510.0650.2190.1370.141
language0.009-0.092-0.031-0.0350.0320.1320.008-0.0130.0160.0580.0150.0951.0000.043-0.034-0.0400.1240.036-0.0670.0880.1270.0150.2410.2860.6720.1500.1040.1320.0960.0650.0480.1270.0440.0290.0700.0690.1440.1680.131
original_selling_amount0.006-0.318-0.076-0.044-0.110-0.1760.4450.2220.2580.1610.219-0.1310.0431.000-0.032-0.0420.4580.2980.6120.7520.0130.0000.0000.0000.0340.0000.0040.0000.0100.0000.0000.0000.0000.0130.0000.0000.0000.0130.018
hotel_area_code-0.0000.0210.0080.0070.0060.013-0.002-0.0170.012-0.018-0.0050.020-0.034-0.0321.0000.013-0.0200.006-0.000-0.0440.0500.0110.0370.1000.0780.0350.0490.0240.0190.0190.0330.0130.0170.0210.0060.0160.0150.0410.033
hotel_city_code0.002-0.0170.0030.0040.0260.067-0.024-0.0010.0130.017-0.0110.021-0.040-0.0420.0131.0000.0170.0170.009-0.0700.0610.0150.0550.0950.1720.0400.1080.0530.0140.0220.0340.0170.0040.0190.0280.0220.0570.0720.053
distance_booking_checkin-0.003-0.614-0.0100.006-0.034-0.1060.1120.0690.1190.1250.085-0.1020.1240.458-0.0200.0171.0000.1580.3430.3090.0450.0080.0400.0270.0810.1220.0750.0300.1740.0310.0470.1300.0400.0420.0240.1010.0610.2890.279
amount_guests0.003-0.121-0.048-0.049-0.0380.0950.1150.1580.9030.4320.4990.0750.0360.2980.0060.0170.1581.0000.029-0.0990.0300.1160.0000.0240.0390.0140.0330.0300.0240.0000.0110.0850.0610.0200.0000.0170.0130.0960.059
amount_nights0.003-0.242-0.070-0.019-0.021-0.0740.0600.0160.0100.0490.034-0.092-0.0670.612-0.0000.0090.3430.0291.0000.1340.0320.0000.0070.0120.0880.0180.0140.0340.0320.0100.0400.0140.0090.0440.0000.0220.0330.0950.112
price_per_guest_per_night0.005-0.202-0.029-0.023-0.118-0.2430.5090.215-0.100-0.0150.008-0.1540.0880.752-0.044-0.0700.309-0.0990.1341.0000.0150.0000.0000.0790.0360.0130.0360.0000.0180.0080.0000.0130.0100.0000.0000.0130.0200.0280.041
guest_is_not_the_customer0.0080.0480.0140.0160.0410.1070.0480.0450.0320.0200.0440.1310.1270.0130.0500.0610.0450.0300.0320.0151.0000.0140.0790.0820.1990.0460.0180.1620.0510.0080.0000.0270.0480.0020.0150.0650.0440.0440.046
no_of_extra_bed0.0030.0200.0080.0110.0280.0230.0210.0280.1060.0370.1020.0200.0150.0000.0110.0150.0080.1160.0000.0000.0141.0000.0000.0000.0470.0000.0150.0130.0000.0130.0000.0160.0270.0350.0120.0000.0000.0340.000
original_payment_method0.0040.0480.0200.0230.0660.1440.1000.0550.0000.0250.0080.2250.2410.0000.0370.0550.0400.0000.0070.0000.0790.0001.0000.3410.1630.1230.1150.0960.0800.0270.0780.1150.0580.0090.0360.0720.0960.2370.148
original_payment_type0.0000.0280.0130.0160.0620.1030.0550.0220.0190.0070.0100.3100.2860.0000.1000.0950.0270.0240.0120.0790.0820.0000.3411.0000.3160.1630.0310.0530.0960.0130.0360.0620.0360.0040.0150.0810.0410.0680.053
original_payment_currency0.0090.0850.0560.0550.0670.4630.1080.0860.0360.0440.0350.8060.6720.0340.0780.1720.0810.0390.0880.0360.1990.0470.1630.3161.0000.1990.1350.2180.0990.0680.1530.1470.0740.1170.0900.0970.3940.2050.177
is_user_logged_in0.0000.1130.0110.0150.0580.0750.0290.0220.0180.0060.0200.0790.1500.0000.0350.0400.1220.0140.0180.0130.0460.0000.1230.1630.1991.0000.0460.4790.0340.0320.0720.0440.0180.0000.0360.0920.0320.0960.095
cancellation_policy_code0.0040.0760.0170.0160.1600.2080.1010.0870.0270.0160.0060.1010.1040.0040.0490.1080.0750.0330.0140.0360.0180.0150.1150.0310.1350.0461.0000.0250.0300.0040.0200.0660.0170.0120.0220.0440.0330.3580.313
is_first_booking0.0000.0260.0120.0060.0220.0960.0380.0400.0080.0380.0080.1190.1320.0000.0240.0530.0300.0300.0340.0000.1620.0130.0960.0530.2180.4790.0251.0000.0410.0250.0440.0390.0220.0240.0260.0760.1790.0380.012
request_nonesmoke0.0000.2510.1820.1810.0210.0410.0230.0430.0220.0170.0110.0790.0960.0100.0190.0140.1740.0240.0320.0180.0510.0000.0800.0960.0990.0340.0300.0411.0000.0460.2170.3910.2420.0290.0600.8610.0200.0210.059
request_latecheckin0.0000.0410.0200.0200.0140.0590.0110.0160.0000.0060.0000.0510.0650.0000.0190.0220.0310.0000.0100.0080.0080.0130.0270.0130.0680.0320.0040.0250.0461.0000.1360.0840.0210.0050.0160.3320.0070.0190.018
request_highfloor0.0070.0430.0240.0210.0260.0400.1320.0710.0090.0080.0070.0910.0480.0000.0330.0340.0470.0110.0400.0000.0000.0000.0780.0360.1530.0720.0200.0440.2170.1361.0000.2530.1150.0440.1950.7660.0100.0350.010
request_largebed0.0000.1780.1220.1200.0290.0970.0890.0490.0690.0460.0310.1090.1270.0000.0130.0170.1300.0850.0140.0130.0270.0160.1150.0620.1470.0440.0660.0390.3910.0840.2531.0000.1700.0410.0900.6800.0000.0420.082
request_twinbeds0.0000.0650.0510.0510.0220.0210.0720.0460.0580.0190.0530.0580.0440.0000.0170.0040.0400.0610.0090.0100.0480.0270.0580.0360.0740.0180.0170.0220.2420.0210.1150.1701.0000.0080.0390.4000.0000.0200.008
request_airport0.0000.0500.0140.0120.0040.0180.0280.0260.0220.0110.0220.0320.0290.0130.0210.0190.0420.0200.0440.0000.0020.0350.0090.0040.1170.0000.0120.0240.0290.0050.0440.0410.0081.0000.0060.1650.0100.0160.000
request_earlycheckin0.0000.0340.0210.0190.0140.0420.0450.0230.0000.0200.0000.0510.0700.0000.0060.0280.0240.0000.0000.0000.0150.0120.0360.0150.0900.0360.0220.0260.0600.0160.1950.0900.0390.0061.0000.4230.0000.0000.007
has_request0.0000.1420.0980.0970.0230.0440.0720.0410.0140.0110.0050.0650.0690.0000.0160.0220.1010.0170.0220.0130.0650.0000.0720.0810.0970.0920.0440.0760.8610.3320.7660.6800.4000.1650.4231.0000.0120.0580.083
costumer_guest_same_nation0.0000.0660.0160.0090.0350.1440.0370.0330.0080.0140.0210.2190.1440.0000.0150.0570.0610.0130.0330.0200.0440.0000.0960.0410.3940.0320.0330.1790.0200.0070.0100.0000.0000.0100.0000.0121.0000.0470.018
pay_now0.0000.2820.0260.0230.1630.2120.0960.0460.0590.0910.0470.1370.1680.0130.0410.0720.2890.0960.0950.0280.0440.0340.2370.0680.2050.0960.3580.0380.0210.0190.0350.0420.0200.0160.0000.0580.0471.0000.387
is_cancel0.0040.2650.0210.0220.1170.2000.0510.0620.0450.0430.0270.1410.1310.0180.0330.0530.2790.0590.1120.0410.0460.0000.1480.0530.1770.0950.3130.0120.0590.0180.0100.0820.0080.0000.0070.0830.0180.3871.000

Missing values

2023-06-08T17:33:37.537729image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-06-08T17:33:38.329031image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

h_booking_idbooking_datetimecheckin_datecheckout_datehotel_idhotel_country_codehotel_live_datehotel_star_ratingaccommadation_type_namecustomer_nationalityguest_is_not_the_customerguest_nationality_country_nameno_of_adultsno_of_childrenno_of_extra_bedno_of_roomorigin_country_codelanguageoriginal_selling_amountoriginal_payment_methodoriginal_payment_typeoriginal_payment_currencyis_user_logged_incancellation_policy_codeis_first_bookingrequest_nonesmokerequest_latecheckinrequest_highfloorrequest_largebedrequest_twinbedsrequest_airportrequest_earlycheckinhotel_area_codehotel_city_codehas_requestdistance_booking_checkinamount_guestsamount_nightsprice_per_guest_per_nightcostumer_guest_same_nationpay_nowis_cancel
07861445258918608962294212213888385.02012-10-04 10:03:004.016.0China0China1200231.037.0436.52AlipayCredit CardCNYTrue1False1.00.00.00.00.00.00.058324611.0282.190278121.036.376667FalseTrueFalse
1-31759251106161709192222222235192057.02012-10-01 10:03:003.010.0Japan0Japan100170.026.054.01American ExpressCredit CardJPYTrue2False0.00.00.00.00.00.00.064322490.0-0.65277811.054.010000FalseTrueFalse
2-516620004202838051725725725890189114.02014-06-12 08:05:004.010.0Taiwan0Taiwan2001130.043.099.18VisaCredit CardTWDTrue1False0.00.00.00.00.00.00.029008920.0-0.91388921.049.590000FalseTrueFalse
36165211278500849566256256257236389113.02010-08-31 07:16:000.05.0Turkey0Turkey1001129.046.019.36MasterCardCredit CardTRYFalse0False0.00.00.01.00.00.00.031107441.0-0.56527811.019.360000FalseTrueFalse
4-18530921314209735673224324518708557.02010-07-01 07:38:003.510.0South Korea0South Korea100174.027.0175.52VisaCredit CardKRWTrue2False0.00.00.00.00.00.00.0376022600.0210.13611112.087.760000FalseFalseTrue
58478973924173415354172196198788472109.02015-01-15 15:54:003.010.0South Korea0South Korea200174.027.068.38UnionPay - CreditcardCredit CardKRWFalse0False0.00.00.00.00.00.00.0170916360.023.03194422.017.095000FalseTrueFalse
6-18534377993681625802082312331634464109.02016-12-19 12:24:003.510.0South Korea0South Korea100174.08.0163.60VisaCredit CardKRWTrue1False1.00.00.00.00.00.01.0296424772.022.69236112.081.800000FalseFalseTrue
72593597287179571502052222253002333109.02017-11-11 20:57:002.010.0Thailand0Thailand2001125.045.048.54VisaCredit CardTHBFalse1False1.00.00.01.00.00.00.0296424772.016.16736123.08.090000FalseFalseFalse
8-1624588124260700023237258260116085061.02016-01-27 12:39:003.010.0South Korea0South Korea300174.027.0119.11UnionPay - CreditcardCredit CardKRWFalse2True1.00.00.00.01.00.00.0390124592.020.72291732.019.851667FalseTrueTrue
9870348569989832933511323023130489338.02010-11-17 07:09:003.510.0China0China200131.037.087.78UNKNOWNInvoiceCNYFalse2True0.00.00.00.00.00.00.0480627450.0116.02847221.043.890000FalseTrueFalse
h_booking_idbooking_datetimecheckin_datecheckout_datehotel_idhotel_country_codehotel_live_datehotel_star_ratingaccommadation_type_namecustomer_nationalityguest_is_not_the_customerguest_nationality_country_nameno_of_adultsno_of_childrenno_of_extra_bedno_of_roomorigin_country_codelanguageoriginal_selling_amountoriginal_payment_methodoriginal_payment_typeoriginal_payment_currencyis_user_logged_incancellation_policy_codeis_first_bookingrequest_nonesmokerequest_latecheckinrequest_highfloorrequest_largebedrequest_twinbedsrequest_airportrequest_earlycheckinhotel_area_codehotel_city_codehas_requestdistance_booking_checkinamount_guestsamount_nightsprice_per_guest_per_nightcostumer_guest_same_nationpay_nowis_cancel
46917-395537070784762775614718618928931745.02011-10-26 16:50:001.09.0China0China200131.037.0116.77WeChatCredit CardCNYTrue0False0.00.00.00.00.00.00.052621420.038.46805623.019.461667FalseTrueFalse
46918-72802540025534940068027027119693157.02011-06-29 10:05:004.010.0South Korea0South Korea100174.027.0196.43MasterCardCredit CardKRWTrue2False0.00.00.00.00.00.00.0272213350.0189.23402811.0196.430000FalseFalseTrue
4691980388618491940519673224324581276257.02015-03-11 14:43:003.010.0South Korea0South Korea100174.027.0132.78VisaCredit CardKRWFalse2True0.00.00.00.00.00.00.0376022600.0210.14305612.066.390000FalseFalseTrue
46920-568784848909774685318021521886863257.02015-02-26 16:35:001.09.0South Korea0South Korea100174.027.087.01VisaCredit CardKRWTrue2False0.00.00.00.00.00.00.039502200.034.15486113.029.003333FalseTrueTrue
469214805148141901219862173245247313284461.02017-11-06 16:47:003.010.0Taiwan0Taiwan3001130.043.0183.54VisaCredit CardTWDTrue2False0.00.00.00.00.00.00.052427310.071.73541732.030.590000FalseTrueFalse
469227859361556233200164258259260250424057.02017-07-08 14:23:003.018.0Japan0Japan200170.026.0142.68UNKNOWNCredit CardJPYFalse2True1.00.00.01.00.00.00.018520392.00.06319421.071.340000FalseTrueFalse
469232778941468684758600265270272261317357.02017-08-15 07:07:002.05.0South Korea0South Korea100174.027.049.74MasterCardCredit CardKRWTrue1False0.00.00.00.00.00.00.0446325670.04.05416712.024.870000FalseTrueTrue
46924-89527943036527181619520320527600861.02014-06-19 08:05:003.510.0Taiwan0Taiwan3001130.043.0140.12VisaCredit CardTWDTrue2False0.00.00.00.00.00.00.012407310.0107.05486132.023.353333FalseFalseTrue
46925-42863049013160776292122132151573295114.02016-10-04 13:32:003.010.0Taiwan1Taiwan2001130.043.087.30MasterCardCredit CardTWDTrue2False1.00.00.00.00.00.00.0520820041.00.45833322.021.825000FalseTrueFalse
4692685160144713308046252072412429173752.02009-06-28 02:02:003.510.0Bangladesh1Bangladesh200112.08.058.01MasterCardCredit CardUSDTrue2False0.00.00.01.00.00.00.0259723021.033.34027821.029.005000FalseFalseFalse